opencv-模板匹配

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;

//-----------------------------------【宏定义部分】--------------------------------------------
//  描述：定义一些辅助宏
//------------------------------------------------------------------------------------------------
#define WINDOW_NAME1 "【原始图片】"        //为窗口标题定义的宏
#define WINDOW_NAME2 "【匹配窗口】"        //为窗口标题定义的宏

//-----------------------------------【全局变量声明部分】------------------------------------
//          描述：全局变量的声明
//-----------------------------------------------------------------------------------------------
Mat g_srcImage; Mat g_templateImage; Mat g_resultImage;
int g_nMatchMethod;
int g_nMaxTrackbarNum = 5;

//-----------------------------------【全局函数声明部分】--------------------------------------
//          描述：全局函数的声明
//-----------------------------------------------------------------------------------------------
void on_Matching( int, void* );
static void ShowHelpText( );

//-----------------------------------【main( )函数】--------------------------------------------
//          描述：控制台应用程序的入口函数，我们的程序从这里开始执行
//-----------------------------------------------------------------------------------------------
int main(  )
{
//【0】改变console字体颜色
system("color 1F");

//【0】显示帮助文字
ShowHelpText();

//【1】载入原图像和模板块
g_srcImage = imread( "1.jpg", 1 );
g_templateImage = imread( "2.jpg", 1 );

//【2】创建窗口
namedWindow( WINDOW_NAME1, CV_WINDOW_AUTOSIZE );
namedWindow( WINDOW_NAME2, CV_WINDOW_AUTOSIZE );

//【3】创建滑动条并进行一次初始化
createTrackbar( "方法", WINDOW_NAME1, &g_nMatchMethod, g_nMaxTrackbarNum, on_Matching );
on_Matching( 0, 0 );

waitKey(0);
return 0;

}

//-----------------------------------【on_Matching( )函数】--------------------------------
//          描述：回调函数
//-------------------------------------------------------------------------------------------
void on_Matching( int, void* )
{
//【1】给局部变量初始化
Mat srcImage;
g_srcImage.copyTo( srcImage );

//【2】初始化用于结果输出的矩阵
int resultImage_cols =  g_srcImage.cols - g_templateImage.cols + 1;
int resultImage_rows = g_srcImage.rows - g_templateImage.rows + 1;
g_resultImage.create( resultImage_cols, resultImage_rows, CV_32FC1 );

//【3】进行匹配和标准化
matchTemplate( g_srcImage, g_templateImage, g_resultImage, g_nMatchMethod );
normalize( g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat() );

//【4】通过函数 minMaxLoc 定位最匹配的位置
double minValue; double maxValue; Point minLocation; Point maxLocation;
Point matchLocation;
minMaxLoc( g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat() );

//【5】对于方法 SQDIFF 和 SQDIFF_NORMED, 越小的数值有着更高的匹配结果. 而其余的方法, 数值越大匹配效果越好
if( g_nMatchMethod  == CV_TM_SQDIFF || g_nMatchMethod == CV_TM_SQDIFF_NORMED )
{ matchLocation = minLocation; }
else
{ matchLocation = maxLocation; }

//【6】绘制出矩形，并显示最终结果
rectangle( srcImage, matchLocation, Point( matchLocation.x + g_templateImage.cols , matchLocation.y + g_templateImage.rows ), Scalar(0,0,255), 2, 8, 0 );
rectangle( g_resultImage, matchLocation, Point( matchLocation.x + g_templateImage.cols , matchLocation.y + g_templateImage.rows ), Scalar(0,0,255), 2, 8, 0 );

imshow( WINDOW_NAME1, srcImage );
imshow( WINDOW_NAME2, g_resultImage );

}

static void ShowHelpText()
{

//输出一些帮助信息
printf("\t欢迎来到【模板匹配】示例程序~\n");
printf("\n\n\t请调整滑动条观察图像效果\n\n");
printf(  "\n\t滑动条对应的方法数值说明: \n\n"
"\t\t方法【0】- 平方差匹配法(SQDIFF)\n"
"\t\t方法【1】- 归一化平方差匹配法(SQDIFF NORMED)\n"
"\t\t方法【2】- 相关匹配法(TM CCORR)\n"
"\t\t方法【3】- 归一化相关匹配法(TM CCORR NORMED)\n"
"\t\t方法【4】- 相关系数匹配法(TM COEFF)\n"
"\t\t方法【5】- 归一化相关系数匹配法(TM COEFF NORMED)\n" );
}